In this paper, we introduce BEV-LaneDet, a robust and real-time framework that achieves 3D lane detection from single image shown in Figure. We establish Virtual Camera to unify the intrinsic and extrinsic parameters of front-facing cameras in different vehicles by homography method[1]}. This module guarantees the consistency of the spatial relationship of front-facing cameras in different vehicles. After applying Virtual Camera in 2D image, there is no need to incorporate intrinsic and extrinsic parameters for features in both the training and inference phases. Moreover, inspired by ASPP[2]}, we present Spatial Transformation Pyramid, a light-weighed module based on MLP transforms multiscale front-view featmaps to bird-eye-view, which can provide robust bird-eye-view features for 3D lane detection. Inspired by [3]}, [4]}, we propose YOLO-Style Representation module as our lane anchor representation. We demonstrate that is uncomplicated and effective module to represent 3D lanes in the bird-eye-view plane. In our experiments, we perform extensive studies to confirm that our BEV-LaneDet significantly outperforms the previous state-of-the-art [5]} in terms of F-score, by up to nearly 10% in OpenLane test set.
<FIGURE>